Klaus-Dietz Tönnies

Surface Topography Quantification by Integral and Feature-related Parameters

Using topographical images obtained by confocal laser scanning microscopy, the topography of brittle fracture surfaces and wire-eroded surfaces was quantified. The global topometry values show a significant dependency on the imaging conditions and on the computing algorithm. An algorithm was developed and implemented for the automatic detection and measuring of feature-related parameters in topographies, which uses methods of computational geometry. The software was tested using brittle fracture surfaces of steel. Quantifizieren von Oberflachentopographien mit globalen und objektbezogenen Parametern Die Topographie von Sprodbruchflachen und drahterodierten Oberflachen wurde anhand von Topographiebildern quantifiziert, die mit der konfokalen Laserrastermikroskopie erhalten wurden. Die globalen Parameter besitzen eine deutliche Abhangigkeit von den Abbildungsbedingungen, insbesondere von der Grose und der Form der Voxel, sowie den Algorithmen. Zum objektspezifischen Quantifizieren wurde eine Software auf der Basis von Methoden der algorithmischen Geometrie entwickelt, mit der planare Flachen in Topographien automatisch detektiert und vermessen werden konnen.

Measurement of the Aortic Diameter in Plain Axial Cardiac Cine MRI

We address the task of aortic diameter measurement in (noncontrast- enhanced) plain axial cardiac cine MRI. To this end, we set up a likelihood maximization problem which allows us to recover globally optimal aorta locations and diameters of the cine sequence efficiently. Our approach provides intuitive means of manual post-correction and requires little user interaction, making large-scale image analysis feasible. Experiments on a data set of 20 cine sequences with 30 time frames showed (at least) pixel-accurate diameter measurements which are also highly stable against re-parameterization.

AngioUnet - A Convolutional Neural Network for Vessel Segmentation in Cerebral DSA Series

The U-net is a promising architecture for medical segmentation problems. In this paper, we show how this architecture can be effectively applied to cerebral DSA series. The usage of multiple images as input allows for better distinguishing between vessel and background. Furthermore, the U-net can be trained with a small corpus when combined with useful data augmentations like mirroring, rotation, and additionally biasing. Our variant of the network achieves a DSC of 87.98% on the segmentation task. We compare this to different configurations and discuss the effect on various artifacts like bones, glue, and screws.

Extracting the Aorta Centerline in Contrast-Enhanced MRI

We propose a semi-automatic approach for aorta centerline extraction in contrast-enhanced MRI, making aorta length analysis feasible on large scale. Starting from user-specified start and end regions, we extract the aorta path in between the regions automatically. The extraction is formulated as an optimization problem, seeking for the path that most likely runs central to the aorta. To this end, we exploit that the aorta distinguishes from the surrounding by strong image gradients that point inwards to the aorta’s center due to contrast-enhanced imaging. We also include additional means of manual guidance to resolveerroneous cases. Experiments on data of 19 subjects yielded results that are close to the inter-reader variability. The average distance to the ground truth was 1.89 ± 1.54 mm, while aorta lengths deviated by only 0.66 ± 0.49 %.

A Learning-Free Approach to Whole Spine Vertebra Localization in MRI

In recent years, analysis of magnetic resonance images of the spine gained considerable interest with vertebra localization being a key step for higher level analysis. Approaches based on trained appearance - which are de facto standard - may be inappropriate for certain tasks, because processing usually takes several minutes or training data is unavailable. Learning-free approaches have yet to show there competitiveness for whole-spine localization. Our work fills this gap. We combine a fast engineered detector with a novel vertebrae appearance similarity concept. The latter can compete with trained appearance, which we show on a data set of 64 \(T_1\)- and 64 \(T_2\)-weighted images. Our detection took \(27.7 \pm 3.78\) s with a detection rate of 96.0 % and a distance to ground truth of \(3.45 \pm 2.2\) mm, which is well below the slice thickness.

Information Efficient Automatic Object Detection and Segmentation using Cosegmentation, Similarity based Clustering, and Graph Label Transfer

We tackle the problem of unsupervised object cosegmentation combining automatic image selection, cosegmentation, and knowledge transfer to yet unlabelled images. Furthermore, we overcome the limitations often present in state-of-the-art methods in object cosegmentation, namely, high complexity and poor scalability w.r.t. image set size. Our proposed approach is robust, reasonably fast, and scales linearly w.r.t. the image set size. We tested our approach on two commonly used cosegmentation data sets and outperformed some of the state-of-the-art methods using significantly less information than possible. Additionally, results indicate the applicability of our approach on larger image sets.

Aorta Segmentation in Axial Cardiac Cine MRI via Graphical Models

We propose an automatic approach to aorta segmentation in axial cardiac cine MRI. The segmentation task is formulated as a prob- abilistic inference problem, seeking for the most probable constellation of aorta locations and shapes in time. To this end, a graphical model is developed that implements the mutual dependencies of the aorta param- eters along the cine sequence. Our approach integrates effective means of manual guidance for post-correction in case of erroneous results, re- quiring only user interaction where necessary. Experiments on a data set of 20 cine sequences showed average Dice coefficients close to the inter- reader variability while outperforming previous work in the field. Only two post-corrections were required for the entire data set. Results also indicate high stability of our approach w.r.t. re-parameterization.

Spotting Differences Among Observations

Identifying differences among the sample distributions of different observations is an important issue in many fields ranging from medicine over biology and chemistry to physics. We address this issue, providing a general framework to detect difference spots of interest in feature space. Such spots occur not only at various locations, they may also come in various shapes and multiple sizes, even at the same location. We deal with these challenges in a scale-space detection framework based on the density function difference of the observations. Our framework is intended for semi-automatic processing, providing human-interpretable interest spots for further investigation of some kind, e.g., for generating hypotheses about the observations. Such interest spots carry valuable information, which we outline at a number of classification scenarios from UCI Machine Learning Repository; namely, classification of benign/malign breast cancer, genuine/forged money and normal/spondylolisthetic/disc-herniated vertebral columns. To this end, we establish a simple decision rule on top of our framework, which bases on the detected spots. Results indicate state-of-the-art classification performance, which underpins the importance of the information that is carried by these interest spots.

Density Difference Detection with Application to Exploratory Visualization

Identifying differences among the distribution of samples of different observations is an important issue in many research fields. We provide a general framework to detect these difference spots in d-dimensional feature space. Such spots occur not only at various locations, they may also come in various shapes and multiple sizes, even at the same location. We address these challenges by a scale-space representation of the density function difference of the observations in feature space. Using three classification scenarios from UCI Machine Learning Repository we show that interest spots carry valuable information about a data set. To this end, we establish a simple decision rule on top of our framework. Results indicate state-of-the-art performance, underpinning the importance of the information that is carried by the detected spots. Furthermore, we outline that the output of our framework can be used to guide exploratory visualization of high-dimensional feature spaces.

Mobile 3D wood pile surveying

We present a novel method for photogrammetric wood pile surveying, which runs on mobile devices as well as on desktop computers. The demand for measurement techniques for wood piles has strongly increased in the last years. Unlike existing methods, our method is not limited to a single image and uses 3D reconstruction techniques on a set of images taken with a smartphone or digital camera. The reconstructed 3D model is then used to identify individual wood logs and perform photogrammetric surveying of the entire wood pile. An extensive evaluation is conducted on 246 data sets (7655 images) from the publicly available HAWK-wood database. For the wood log detection benchmark a true positive rate of 98.8% with a false positive rate of 0.7% is achieved. The volume computation showed an average absolute difference of 2.2% (contour volume) and 5.6% (solid wood volume).